Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years

Climate change–induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

represent the 95% confidence intervals of 1,000 bootstraps.The "r" is the correlation coefficient, with an asterisk indicating significance at 95%.The factors include annual precipitation (aPr), N input (Nin), soil bulk density (BD), and relative precipitation differences between two consecutive years (diffPr).Irrigated (IRR) and rainfed (RFD) conditions are distinguished for maize (a-h), rice (i-p), and wheat (q-x).Table S3.Comparison of ratio of nitrogen losses (Nw, Tg N yr -1 ) to nitrogen input (Nin, Tg N yr -1 ).

Fig. S8 .SSP370Fig. S9 .
Fig. S8.Changes in future annual precipitation (aPr) relative to 1981-2010 average.'Average' means changes in aPr average over future 30 years, weighted by food production unit (FPU) cropland areas, relative to the 1981-2010 average; 'Extremely' means changes in aPr average over future extremely wet years relative to the 1981-2010 average.IRR: irrigated; RFD: rainfed.

Fig. S10 .
Fig. S10.Similar to Fig. 3 in the main text, but based on the scenario SSP126.Irrigated (IRR) and rainfed (RFD) are distinguished for three crops: maize, rice, and wheat.

Fig. S11 .
Fig. S11.Correlation between extreme nitrogen losses (Nw) and driving factors.Each point represents the extreme Nw of a food production unit against various factors averaged over historical extremely wet years.The solid lines indicate the best-fit linear regressions between extreme Nw and driving factors, while the shaded areas

Fig. S14 .
Fig. S14.Relations between annual precipitation (aPr) anomaly and relative precipitation difference between two consecutive years (diffPr).Equations show the linear regression between aPr and diffPr with coefficient of determination (R 2 ) and p-value presented.Red lines are the linear regression lines.The relations are presented for three crops: maize (a, b), rice (c, d), and wheat (e, f).

Fig. S15 .
Fig. S15.Absolute extreme aquatic nitrogen loss (Nw) changes during the historical period.Silver areas represent the food production units with no extremely wet years and white areas represent no cropland areas.Irrigated (IRR) and rainfed (RFD) are distinguished for three crops: maize (a, b), rice (c, d), and wheat (e, f).

Fig. S16 .
Fig. S16.Performance of Random Forest (RF) on representing PEPIC simulated crop yield and aquatic nitrogen losses (Nw).Dashed lines show 1:1 line.R 2 : coefficient of determination; RMSE: root mean square error; ntree and mtry are the RF parameters.The comparison is presented for three crops: maize (a, d), rice (b, e), and wheat (c, f).

Fig. S17 .
Fig. S17.Mitigation of extreme nitrogen losses (Nw).The heat maps show a) reduction in nitrogen input (Nin) over the 1981-2010 period, b) reduction in Nw over the extremely wet years, and c) reduction in crop yield over the 1981-2010 period resulting from a reduction of Nin to a certain level relative to current inputs.'Entire' means Nin is scaled down over the entire period, while 'diffPr' means Nin is scaled down only over years with relative precipitation difference between two consecutive years (diffPr) higher than a certain threshold.Reductions in Nin, Nw, and crop yields are relative to their respective 1981-2010 average.The three crops under both irrigated and rainfed conditions are aggregated by area-weighted averages.

Fig. S18 .
Fig. S18.Similar to Fig. 5 in the main text, but without any compromises on crop yield.Irrigated (IRR) and rainfed (RFD) are distinguished for three crops: maize, rice, and wheat.

Fig. S19 .
Fig. S19.Mitigation of future extreme nitrogen losses (Nw) distinguished by irrigated (IRR) and rainfed (RFD) conditions.The mitigation measures are achieved by using the diffPr thresholds and N input scaling ratios shown in fig.S18.

Fig. S20 .
Fig. S20.Similar to Fig. 5 in the main text, but for the scenario SSP126.Irrigated (IRR) and rainfed (RFD) are distinguished for three crops: maize, rice, and wheat.

Fig. S21 .
Fig. S21.Proportion of the diffPr thresholds and scaling ratios of nitrogen input of the measures in Fig. 5 in the main text.Rows are designated as IRR for irrigated areas (a, b) and RFD for rainfed areas (c, d).

Fig. S22 .
Fig. S22.Mitigation of future extreme nitrogen losses.The figure shows changes of the future extreme nitrogen losses (Nw), of the 2036-2065 average nitrogen input (Nin) and crop yield with mitigation vs. no-mitigation relative to the 1981-2010 averages.The mitigation is achieved through using different optimal combinations of diffPr thresholds and Nin scaling ratios by setting different constraints on the reductions of Nin (10%, 15%, and 20%) and of yield (3% and 5%).Bars show the mean changes of different constraints, while error lines are the standard deviation.Irrigated (IRR) and rainfed (RFD) are distinguished for three crops: maize, rice, and wheat.

Fig. S23 .
Fig. S23.One dimensional partial dependence plots of aquatic nitrogen losses (Nw) to different influencing factors according to the Random Forest simulations.Nin: N fertilizer inputs (a-c); aPr: annual precipitation (d-f); aT: annual temperature (g-i); DB: soil bulk density (j-l); diffPr: relative precipitation difference between two

Fig. S24 .
Fig. S24.Relative importance of the Random Forest models for simulating aquatic nitrogen losses of maize (a), rice (b), and wheat (c).Nin: N input; aPr: annual precipitation; ferPr: precipitation during N fertilization period; aT: annual temperature; STC: silt content; CF: coarse fragment; DB: soil bulk density; SDC: sand content; diffPr: relative precipitation difference between two consecutive years; IRRF: distinguishing irrigated or rainfed cultivations; Extreme: distinguishing extremely wet years or other years.

Fig. S26 .
Fig. S26.Performance of PEPIC on simulating crop yield at the country level for maize (a), rice (b), and wheat (c).Equations show the linear regression between reported yields (from FAOSTAT) and simulated yields by PEPIC with coefficient of determination (R 2 ).Red dashed lines are the linear regression lines and blue ones are the 1:1 line.Colors represent different continents and sizes represent cropland areas for each country.

Fig. S27 .
Fig. S27.Correlation between normalized observed total nitrogen (N) concentration in the water bodies and normalized aquatic nitrogen losses (Nw) simulated by the PEPIC model.Only the regions with observed total N concentration over 10 years were considered.

Table S1 . Proportion of extremely wet years (EWYs) to all years in the historical and future periods
. IRR: irrigated; RFD: rainfed.